Issue Of Service Composition Research Articles

Web service embedding: Representing the invocation association between services with practical-valued vectors

Service representation using specified methods to express the service’s functionalities and non-functionalities in a machine-understandable format is crucial in service composition. In representing non-functionalities, existing approaches focus on QoS (Quality of Service) or sociability. The invocation association between services has not been considered in service representation. However, as the invocation association reflects the actual invocation situation on service execution, the inconsideration will increase the failure of service composition. Thus, finding an appropriate method to represent the invocation association is a significant issue in service composition. This paper proposes a novel service representation method called web service embedding (WSE) to generate a practical-valued vector representation using deep learning sequence models to address the challenge. First, we propose utilizing deep learning sequence models to capture the invocation association from service composition/invocation sequences. Next, the representation vectors of services can be generated using the pre-trained models by designing an unsupervised training solution. Then, two presented metrics, context invocation relevance, and semantic invocation relevance, are used to measure how well the embedding vectors of the models express the invocation association for clustering in service composition. Finally, various neural sequence architectures have been inspected to find suitable models for WSE. The experimental results show that our proposed WSE can successfully perform service representation for clustering based on the invocation association.

An Adaptive Multiobjective Multitask Service Composition Approach Considering Practical Constraints in Fog Manufacturing

Fog manufacturing (FMfg) is an emerging real-life cloud manufacturing (CMfg) paradigm based on digital twin models, which perceive finely dynamic processes and provide on-demand optimization services. Service composition as a core plays a vital role in achieving the optimization goal. Service composition issues in actual CMfg are mostly multitask problems that need considering dynamic factors. Otherwise, the planned production process will deviate significantly from actual situations. However, such multitask service composition (MTSC) models have scarcely been studied. Thus, this article formulates a multiobjective MTSC model considering the crucial resource competition process between multitasks under multiple practical constraints (MPC). Then, to solve the MTSC–MPC, this article develops an adaptive multiobjective whale optimization algorithm (AMOWOA) containing well-designed strategies. Numerical experiments and application cases verify that AMOWOA outperforms the comparison algorithms. AMOWOA continually can optimize and adaptively adjust the service composition process based on the crucial manufacturing resource available time under actual constraints in FMfg environment.

Logistics-involved service composition in a dynamic cloud manufacturing environment: A DDPG-based approach

Service composition as an important technique for combining multiple services to construct a value-added service is a major research issue in cloud manufacturing. Highly dynamic environments present great challenges to cloud manufacturing service composition (CMfg-SC). Most of previous studies employ heuristic algorithms to solve service composition issues in cloud manufacturing, which, however, are designed for specific problems and lack adaptability necessary to dynamic environment. Hence, CMfg-SC calls for new adaptive approaches. Recent advances in deep reinforcement learning (DRL) provide a new means for solving this issue. Based on DRL, we propose a Deep Deterministic Policy Gradient (DDPG)-based service composition approach to cloud manufacturing, with which optimal service composition solutions can be learned through repeated training. Performance of DDPG in solving CMfg-SC in both static and dynamic environments is examined. Results obtained with another DRL algorithm - Deep Q-Networks (DQN) and the traditional Ant Colony Optimization (ACO) are also presented. Comparison indicates that DDPG has better adaptability, robustness, and extensibility to dynamic environments than ACO, although ACO converges faster and its steady QoS value of the service composition solution is higher than that of DDPG by 0.997%. DDPG outperforms DQN in convergence speed and stability, and the QoS value of the service composition solution of DDPG is higher than that of DQN by 3.249%.

An Intelligent Cloud Service Composition Optimization Using Spider Monkey and Multistage Forward Search Algorithms

Web service composition allows developers to create and deploy applications that take advantage of the capabilities of service-oriented computing. Such applications provide the developers with reusability opportunities as well as seamless access to a wide range of services that provide simple and complex tasks to meet the clients’ requests in accordance with the service-level agreement (SLA) requirements. Web service composition issues have been addressed as a significant area of research to select the right web services that provide the expected quality of service (QoS) and attain the clients’ SLA. The proposed model enhances the processes of web service selection and composition by minimizing the number of integrated Web Services, using the Multistage Forward Search (MSF). In addition, the proposed model uses the Spider Monkey Optimization (SMO) algorithm, which improves the services provided with regards to fundamentals of service composition methods symmetry and variations. It achieves that by minimizing the response time of the service compositions by employing the Load Balancer to distribute the workload. It finds the right balance between the Virtual Machines (VM) resources, processing capacity, and the services composition capabilities. Furthermore, it enhances the resource utilization of Web Services and optimizes the resources’ reusability effectively and efficiently. The experimental results will be compared with the composition results of the Smart Multistage Forward Search (SMFS) technique to prove the superiority, robustness, and effectiveness of the proposed model. The experimental results show that the proposed SMO model decreases the service composition construction time by 40.4%, compared to the composition time required by the SMFS technique. The experimental results also show that SMO increases the number of integrated ted web services in the service composition by 11.7%, in comparison with the results of the SMFS technique. In addition, the dynamic behavior of the SMO improves the proposed model’s throughput where the average number of the requests that the service compositions processed successfully increased by 1.25% compared to the throughput of the SMFS technique. Furthermore, the proposed model decreases the service compositions’ response time by 0.25 s, 0.69 s, and 5.35 s for the Excellent, Good, and Poor classes respectively compared to the results of the SMFS Service composition response times related to the same classes.

SPASC: Strategy‐proof auction mechanism with cost and QoS incentive for service composition

AbstractIn cloud computing, service composition provides an appealing paradigm to compose existing single services into a powerful workflow. The critical issues in service composition is that service providers may lie about the service's cost and QoS for more profits. However, existing studies only address the issue of services' untruthful cost, none of the studies consider both the issue of untruthful cost and untruthful QoS. Thus, it is necessary to design an incentive mechanism to motivate service providers to offer services with the truthful cost and truthful QoS. To address the above challenging issues, we propose the strategy‐proof auction mechanism for service composition (SPASC), which can determine the winners and dynamically calculate the price of services. To the best of our knowledge, this is the first study to design an incentive mechanism to motivate service providers to offer services with the truthful cost and truthful QoS in this field. Furthermore, we propose an auction algorithm to implement the SPASC. Both theoretical proof and the extensively experimental results validate that SPASC achieves the truthfulness and individual rationality. At the same time, the experimental results also prove that this mechanism can motivate service providers to offer services with the truthful cost and truthful QoS.

QoS-Aware Service Composition in Cloud Manufacturing: A Gale–Shapley Algorithm-Based Approach

Cloud manufacturing (CMfg) is an emerging paradigm that aims to provide on-demand manufacturing services over the Internet. Service composition as an important means for generating value-added services plays an important role in achieving the aim of CMfg. Most of previous works focused on exploring techniques of service composition for a single composite task using meta-heuristic algorithms. However, the issue of service composition for multiple composite tasks has rarely been considered. Meta-heuristic algorithms suffer from cumbersome parameter tuning as well as the tendency of getting into local optima. In addition, the effectiveness of different algorithms has not yet been fully explored when different degrees of constraints are imposed. Different from approaches in most of the previous works, this paper proposes an extended Gale–Shapley (GS) algorithm-based approach for service composition that allows generation of multiple service composition solutions effectively. Requirements with different constraints are considered. Experimental results indicate that: 1) meta-heuristic algorithms can be used in various scenarios with different degrees of constraints. However, they are incapable of finding the optimal solutions in situations with relatively loose constraints, and moreover, the failure rate of finding solutions for a batch of multiple tasks is high; 2) the dynamic programming (DP) is a method that is the most sensitive to constraints. It performs better only under loose constraints and in the case of a single requirement; and 3) the application range of the GS method proposed is wider than that of the DP method. It can achieve better performance when constraints are relaxed irrespective of task status (i.e., a single task or multiple tasks), and moreover, it can make more tasks find solutions in the multitask scenario without service reuse.

Logistics-involved QoS-aware service composition in cloud manufacturing with deep reinforcement learning

• Deep reinforcement learning is used to solve the QoS-aware service composition problem in cloud manufacturing with the consideration of logistics. • A DRL algorithm that combines the basic DQN algorithm, the dueling architecture, and the prioritized replay mechanism is proposed. • Experimental results demonstrate the effectiveness, high efficiency, adaptability, and scalability of the proposed DRL algorithm. Cloud manufacturing is a new manufacturing model that aims to provide on-demand manufacturing services to consumers over the Internet. Service composition is an essential issue as well as an important technique in cloud manufacturing (CMfg) that supports construction of larger-granularity, value-added services by combining a number of smaller-granularity services to satisfy consumers’ complex requirements. Meta-heuristics algorithms such as genetic algorithm, particle swarm optimization, and ant colony algorithm are frequently employed for addressing service composition issues in cloud manufacturing. These algorithms, however, require complex design flows and painstaking parameter tuning, and lack adaptability to dynamic environment. Deep reinforcement learning (DRL) provides an alternative approach for solving cloud manufacturing service composition (CMfg-SC) issues. DRL as model-free artificial intelligent methods enables a system to learn optimal service composition solutions through training, which can therefore circumvent the aforementioned problems with meta-heuristics algorithms. This paper is dedicated to exploring possible applications of DRL in CMfg-SC. A logistics-involved QoS-aware DRL-based CMfg-SC is proposed. A dueling Deep Q-Network (DQN) with prioritized replay named PD-DQN is designed as the DRL algorithm. Effectiveness, robustness, adaptability, and scalability of PD-DQN are investigated, and compared with that of the basic DQN and Q-learning. Experimental results indicate that PD-DQN is able to effectively address the CMfg-SC problem.

A two-layer social network model for manufacturing service composition based on synergy: A case study on an aircraft structural part

Abstract Cloud manufacturing is an emerging manufacturing paradigm, with the issue of service composition being one of its most significant challenges. The current researches take the view that all manufacturing services are of equal importance. However, in fact, manufacturing services that owns scarce resources are more important than ordinary one, especially in the production of complex products. Hence, it is significant to meet the requirements of manufacturing services that own scarce resources first. Meanwhile, due to the increasing complexity of products, a high degree of synergy is required in service composition. Our study explores a new framework of social relationships from the perspective of synergy. A two-layer social network model is first proposed for resource allocation that takes synergy and priority into account. The upper layer of this model is occupied by the manufacturing services with scare resources. The lower layer is occupied by ordinary manufacturing services. All services are connected by social relationships. For the master-slave characteristic of the model, the two-layer network is embedded into bi-level programming. Then, an improved genetic algorithm is designed to solve the problem. Finally, the effectiveness of the model and algorithm are verified using a structural part of the Modern Ark 60 as an example.

Service Composition in IoT using Genetic algorithm and Particle swarm optimization

AbstractWeb service compositions are commendable in structuring innovative applications for different Internet-based business solutions. The existing services can be reused by the other applications via the web. Due to the availability of services that can serve similar functionality, suitable Service Composition (SC) is required. There is a set of candidates for each service in SC from which a suitable candidate service is picked based on certain criteria. Quality of service (QoS) is one of the criteria to select the appropriate service. A standout amongst the most important functionality presented by services in the Internet of Things (IoT) based system is the dynamic composability. In this paper, two of the metaheuristic algorithms namely Genetic Algorithm (GA) and Particle Swarm Optimization (PSO) are utilized to tackle QoS based service composition issues. QoS has turned into a critical issue in the management of web services because of the immense number of services that furnish similar functionality yet with various characteristics. Quality of service in service composition comprises of different non-functional factors, for example, service cost, execution time, availability, throughput, and reliability. Choosing appropriate SC for IoT based applications in order to optimize the QoS parameters with the fulfillment of user’s necessities has turned into a critical issue that is addressed in this paper. To obtain results via simulation, the PSO algorithm is used to solve the SC problem in IoT. This is further assessed and contrasted with GA. Experimental results demonstrate that GA can enhance the proficiency of solutions for SC problem in IoT. It can also help in identifying the optimal solution and also shows preferable outcomes over PSO.

Service Composition Issues in Cyber Physical Systems and Middleware Design

Service Composition Issues in Cyber Physical Systems and Middleware Design

A bargaining-driven global QoS adjustment approach for optimizing service composition execution path

In service-based workflow area, service composition is emerging as a promising technology providing more flexible integration means across a variety of distributed heterogeneous applications. A critical research issue in service composition is how to achieve an optimized overall end-to-end quality of service (QoS) composition’s requirements by effectively coordinating QoS constraints of individual service. However, few of existing representative works have well considered economic strategies closely related to competitive market scenarios to address overall QoS issues in service composition. In this paper, we propose a novel global QoS adjustment approach by employing a recursive bargaining strategy to gradually remove QoS constraint violations for optimizing service composition execution path. Our approach mainly exploits the hidden marketplace competitive techniques from economics for developing a novel bargaining strategy. Based on this strategy, a user agent can simultaneously negotiate with more than one provider for driving them into competition. As a result, concessions may be made by service providers depending on their own economic equilibrium theories to offer better QoS values than their initial proposals and thus a user can win more profits. Through recursively using bargaining strategy, critical nodes of an initially built optimal service composition execution path by local optimization policy can be continuously updated to minimize QoS constraint violations by reselecting better service providers. Eventually, a near-optimal service composition execution path can be reconstructed meeting user’s overall QoS requirements. Meanwhile, an experiment and evaluation have been conducted for the sake of demonstrating the feasibility and effectiveness of our proposed approach.

Service Composition Issues in Pervasive Computing

Combining existing services to create new pervasive computing services involves special design considerations, including context awareness, contingency management, device heterogeneity, and user empowerment. Service composition is a natural concept around which to structure pervasive systems and individuate parts of them that are suitable for contingency management, application, storage, and so on. The notion of service composition also provides a natural frame for addressing systemic issues such as performance, usability, and security. In this case, such characteristics aren't determined by a particular service but rather by the software architecture-how services are combined.

AUTOMATIC SERVICE COMPOSITION VIA SIMULATION

In this paper we study the issue of service composition, for services that export a representation of their behavior in the form of a finite deterministic transition system. In particular, given a specification of the target service requested by the client as a finite deterministic transition system, the problem we face is how we can exploit the computations of the available services for realizing the computations of the target service. While ways to tackle such a problem are known, in this paper we present a new technique that is based on the notion of simulation, which is still optimal from the computational complexity point. Notably, such a technique, opens up the possibility of devising composition in a "just-in-time" fashion. Indeed, we show that, by exploiting simulation, it is actually possible to implicitly compute all possible compositions at once, and delay the choice of the actual composition to run-time.

An Approach to Discover Dependencies between Service Operations

Service composition is emerging as an important paradigm for constructing distributed applications by combining and reusing independently developed component services. One key issue of service composition is how to identify relevant service operations so as to compose services rapidly and correctly. A promising approach to simplifying the search of relevant service operations in service composition lies in the discovery of the dependencies between service operations. However, the discovery of operation dependencies is not a trivial task but a challenge. We propose an approach to discover operation dependencies in a personal problem solving environment. Our approach combines the semantic matching of inputs and outputs interfaces between service operations and the analysis of process cases to identify dependencies. The main contributions of the approach are: 1) It can be used to identify the direction of the dependencies. 2) It provides the method to measure the strength of dependencies. 3) Nonconflict property and non-redundancy property of discovered dependencies are guaranteed based on a dependency graph. Moreover, we experimentally demonstrate the efficacy of our approach by testing it under three typical bioinformatics scenarios.